The present invention generally relates to cloud computing, and more specifically to managing the admission of virtual machines in a cloud environment with dynamic resource demands.
In a Cloud or computing cluster system managing the utilization of computer resources with effective admission control policies is essential. Admission control policies ensure that sufficient resources are available in a cluster to provide fail-over protection and to ensure that virtual machine resource reservations are respected. If the additional computer resources are not reserved, the power-on attempt fails and the fail-over protections cannot be realized. Hence, admission control policies reserve resources to ensure robustness for a potential fail-over and successful power-on procedures.
Each resource in a physical machine in the Cloud system has an over-utilization threshold. When this threshold is exceeded and the resources are over-utilized for longer periods of time, operations of physical machines may be interrupted or migration may become necessary. Over-utilization threshold is the maximum acceptable utilization percentage for a resource. As an example, if the over-utilization threshold is 90%, it is assumed that the operations will not be interrupted, as long as the resource utilization remains below 90%. In addition to over-utilization threshold, the likelihood of the resource being over-utilized is another parameter to be considered for the purpose of admission control. The second threshold is the percentage of time that the over-utilization can be tolerated. In other words, the likelihood of finding the resource over-utilized. Note that the over-utilization threshold can be exceeded for short periods of time. If the likelihood of fail-over or the virtual machine power-on is negligible, exceeding the over-utilization threshold may be tolerated.
Admission control policies adopt admission criteria by which admission control schema accepts or rejects a request to be placed in the Cloud. In general, admission control schemas are either parameter-based or measurement-based. Parameter-based admission control schemas are based on apriori knowledge of the input requests and provide for deterministic guarantees for uninterrupted Cloud operations. Examples of this type of admission control schemas include admitting a VM request based on its resource demand characteristics, such as maximum resource demand or average resource demand. Parameter-based schemas are easy to implement and guarantee Cloud operations under worst-case assumptions. A measurement-based admission control schema, on the other hand, utilizes the estimated resource utilization of physical machines in addition to input VM request parameters. In this case, the utilization of a resource is characterized by its stochastic properties and a probabilistic bound can be defined for the potential interruptions of Cloud operations. The probability density function (pdf) of the utilization of a resource is the convolution of all the resource demands of the accepted requests that utilize that resource. In such aggregation of independent resource demands, the probability that the aggregate utilization will reach the sum of the peak demand is infinitesimally small. Using the pdf of the aggregated resource utilization in admission criteria provides for probabilistic guarantees. That is, instead of providing deterministic bound for the worst case scenarios, measurement-based admission control policies guarantee a bound on the probability of over-utilization. In mathematical terms, resource k is stable if its utilization, Uk, satisfies the following constraint,P(Uk>Uko)≦εo  (1)where Uo is the over-utilization threshold and εo is the probabilistic bound on over-utilization.